e clinical solutions irug 2012 12sep2012
DESCRIPTION
This presentation will provide an overview on how reconciliation and/or validation rules can be defined and trial data can be checked against these rules. By utilizing JReview’s built-in browser and advanced functionalities, objects can be defined with drill-down capabilities to perform these data validation checks. For e.g. reconciliation checks between EDC data and external lab data can be easily performed by reviewing a summary object with discrepant information such as subject id, discrepancy category and discrepancy message with an ability to drill down to detailed discrepant data listings. This approach should support pro-active data management for ongoing trials increasing the overall data quality. Similar approach can be applied to review data against sponsor defined data standards checks.TRANSCRIPT
April 8, 2023Confidential Presentation
Leveraging JReview as a Data Quality Solution
Raj Indupuri &
Chandi Kodthiwada
Agenda
• Data Quality Challenges• JReview Solution Overview• Data Reconciliation Business Case• Data Standards Business Case• Q&A
Data Quality Challenges
Data Reconciliation
•Very tedious
Different sources and systems
Variant structures and formats
Labor intensive
•Access and Ease of use
Different refresh cycles
Error-prone if performed using spreadsheets
•JReview
Interactive with drill-down capabilities
Self-service
Why did it happen?
What’s happening now?
•Proactive Data Management
Ongoing review and verification
•Reusable across trials
Global Objects
Customizable
Data Standards
•Difficult to validate compliance checks ongoing
•Difficult to validate sponsor and protocol related checks
•Difficult to get visibility during trial conduct
Intensive programming and SAS based backend
processes
JReview Solution Overview – How?
Specifications
•Define Categories and Items for creating an analysis friendly discrepancy panel
•Add Notes to provide further insight into the discrepancy
•Conceptualize Run-time parameters
JReview Solution Overview – How?
Design/Programming
•Implement a Materialized View
•Programming will abstract all the source data type disparities & structure variances in source data from end-
user
JReview Integration/Object Development
•Import SQL development [Discrepancy Item Categorization & Identification]
•Develop Objects based on business needs: ranging from Discrepancy metrics per site to Subject level
discrepancy listings
•Slice and Dice data: Allow Object drill-down from a high-level summary to a detail subject level listing
Data Reconciliation - Requirements
6
Define discrepancy details
Category Item NotesSubject Identifiers Subject Initials Subject Initials Mismatch
Date of Birth Date of Birth Mismatch
Sex Sex Mismatch
Visit Discrepancies Visit/Planned Time point Name Not in eCRF Data
Visit/Planned Time point Name Not in External Vendor Data
Data Discrepancies Date/Time of ECG Date Mismatch
ECG Result Result Mismatch
Completion Status Test marked complete but not in
External Vendor Data
Field Name Column Heading Derived Category Derived Item Derived Notes EG.USUBJID/EP.USUBJID Unique Subject ID EG.EGTEST/EP.ECTEST ECG Test Name EG.VISITNUM/EP.VISITNUM Visit Number EG.VISIT/EP.VISIT Visit
EG.EGTPT/EP.ECTPTeCRF Planned Time Point Name
EP.EPTPTExternal Planned Time Point Name
EG.EGSEQ eCRF Sequence Number EP.EPSEQ External Sequence Number
EG.EGDTC eCRF Date/Time of ECG EP.EPDTC External Date/Time of ECG
EG.EGSTAT eCRF Completion Status EP.EPSTAT External Completion Status
EG.EGSTAT1eCRF Completion Status at each Time point
EP.EPSTAT External Completion Status
DM.SEX eCRF Subject Sex EP.EPSEX External Sex
DS.SUBINIT eCRF Subject Initials EP.SUBJINIT External Subject Initials
DM. BRTHDTC eCRF Birth Date EP.EPDOB External Birth Date
EG.EGORRES eCRF Result EP.EPVAL External ECG Evaluation
Variables to reconcile (ECG eCRF vs. ECG External Provider)
Data Reconciliation - Requirements
Data Reconciliation - Objects
8
Data Reconciliation - Objects
9
Data Reconciliation – Design and Develop
10
•So
ur
ce
D
at
as
et
/T
ab
le
•Identify Sources: •
EG (eCRF ECG Data)•
EP (External Vendor ECG Data)
•Vie
w
Pro
gra
m
mi
ng
•Develop a view with aggregated Identifier information from both sources and join the source data
back to the aggregated Identifier information effectively joining data wherever applicable
•Ma
ter
iali
ze
d
Vie
w/T
abl
e
•Performance: Run the view every time? Query a static table [Maintenance] ?
•Im
por
t
SQ
L
•Discrepancy Categorization
•Discrepancy Identification
•JRe
vie
w
Obj
ect
De
vel
op
me
nt
•Build Objects•
Summary, Detailed & Graphs
Data Reconciliation – Merged View
Data Reconciliation – Design and Develop
Import SQL
Data Standards - Requirements
13
Define data standards checks
Data Validation Category
Data Validation ID Data Validation Item Severity
Consistency C0001 Duplicate --SEQ Error
Consistency C0002 Duplicate USUJID, with different SUBJID ErrorPresence SD0001 No records in data source Warning
PresenceSD0069
No Disposition record found for subject Warning
PresenceSD0070
No Exposure record found for subject Warning
PresenceSD0002
Null value in variable marked as Required
Error
Data Standards - Objects
14
Data Standards - Objects
Data Standards – Design and Develop
16
Q & A
17
April 8, 2023Confidential Presentation
Thank You!